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This article is devoted to improving previously developed texture classifier that performs on noisy images. The basic principle of this classifier is to join several simple local parameters using some fuzzy logic system (support vector machine or neural network). It is shown that aggregating procedure applied on the classifier's input can result in significant improvement of its efficiency.
Live work training centres are unique places where electricians learn and develop their professional skills, but also a place where instructors use the latest andragogy approaches and techniques to transfer knowledge. Experts for live work education and training are rare and valuable human resource of every company. Being faced with retirement and other natural workmen fluctuation companies need to...
As a rapidly growing number of open source projects adopt distributed version control systems, it becomes more crucial and challenging for release managers to oversee the software development, testing, deployment, and support during the software development life cycle. Manual monitoring and control of the lengthy commit history has been extremely tedious and effort consuming. This paper proposes an...
The purpose of this study is to clarify the relationship between a rugby player's personality and length of career and personality and position. This study was conducted with 59 male university rugby team members. The psychological tests were classified by athletic position and athletic experience. The data from the tests was analyzed by T-test and chi-squarestest. As a result, there was no significant...
Unmanned systems are increasing in number, while their manning requirements remain the same. To decrease manpower demands, machine learning techniques and autonomy are gaining traction and visibility. One barrier is human perception and understanding of autonomy. Machine learning techniques can result in “black box” algorithms that may yield high fitness, but poor comprehension by operators. However,...
Deep Neural Network (DNN) based transfer learning has been shown to be effective in Visual Object Classification (VOC) for complementing the deficit of target domain training samples by adapting classifiers that have been pre-trained for other large-scaled DataBase (DB). Although there exists an abundance of acoustic data, it can also be said that datasets of specific acoustic scenes are sparse for...
Call Detail Records (CDRs) are a primary source of whereabouts in the study of multiple mobility-related aspects. However, the spatiotemporal sparsity of CDRs often limits their utility in terms of the dependability of results. In this paper, driven by real-world data across a large population, we propose two approaches for completing CDRs adaptively, to reduce the sparsity and mitigate the problems...
Modern machine-learning techniques greatly reduce the efforts required to conduct high-quality program compilation, which, without the aid of machine learning, would otherwise heavily rely on human manipulation as well as expert intervention. The success of the application of machine-learning techniques to compilation tasks can be largely attributed to the recent development and advancement of program...
Feature selection has become a remarkable research topic in recent years. It is an efficient methodology to tackle the information with high dimension. The underlying structure has been neglected by the previous feature choice technique and it determines the feature singly. Considering this truth, we are going to focus on the matter wherever feature possess some cluster structure. To resolve this...
As Convolutional Neural Networks continue to produce state of the art results, more types of data are being used to see the results that would be produced. Using the heart rate data that was collected using sensors from various subjects who consumed alcohol, we converted it from the 1D waveform into a set of spectrograms. The spectrograms were fed into two pretrained CNNs, CaffeNet and AlexNet, to...
In recent years, we have seen a surge of interest in neuromorphic computing and its hardware design for cognitive applications. In this work, we present new neuromorphic architecture, circuit, and device co-designs that enable spike-based classification for speech recognition task. The proposed neuromorphic speech recognition engine supports a sparsely connected deep spiking network with coarse granularity,...
It is hard to predict student test scores in Mathematics. By being able to predict test scores students that will struggle may be identified. These students could be given more attention. This research uses the K-Nearest Neighbor (KNN) algorithm to predict the categorization of Mathematics test scores. The KNN algorithm initiates with a training data set and a value for parameter K. When evaluating...
Against the problems existing in the collective running projects, such as not-reasonable-enough monitoring means, not-scientific-enough assessment tools, and so on, an overseeing and evaluating system of running training was designed and implemented based on the radio frequency identification(RFID), which has the advantages of high precision, comprehensive and real-time positioning. The system provides...
This research presents framework for real time face recognition and face emotion detection system based on facial features and their actions. The key elements of Face are considered for prediction of face emotions and the user. The variations in each facial feature are used to determine the different emotions of face. Machine learning algorithms are used for recognition and classification of different...
Technology use in the Indian classrooms has increased manifolds in the last five years or so. Even though most of the technology initiatives thrive in the private sector or the autonomous higher education institutes, many educational technology companies were launched in the last decade. Smart classrooms, online tutoring, personalised learning content, learning management systems, content delivery...
The main objective of the spatial image classification is to extract information classes from a multiband raster spatial image. The network structure and number of inputs are the key factors in deciding the performance and accuracy of the traditional pixel based image classification techniques like Support Vector Machines (SVM), Artificial Neural Networks (ANN), Fuzzy logic, Decision Trees (DT) and...
In ensemble learning, ensemble pruning is a procedure that aims at removing the unnecessary base classifiers and retaining the best subset of the base classifiers. We presented a two-step ensemble pruning framework, in which the optimal size of the pruned ensemble is first decided, and then with the optimal size as input, the optimal ensemble is selected. For the first step to find the optimal ensemble...
The adequate representation of states in the construction of intelligent agents is fundamental for allowing them to achieve a satisfactory performance, principally for those that actuate in a competitive environment that possesses a high state space. One particular type of representation that is very appropriate for these situations is the NetFeatureMap, which describes by means of features the relevant...
Word2vec is a neural network language model which can convert words and phrases into a high-quality distributed vector (called word embedding) with semantic word relationships, so it offers a unique perspective to the text classification and other natural language processing (NLP) tasks. In this paper, we propose to combine improved tfidf algorithm and word embedding as a way to represent documents...
Detection of string and column delimiters is a critical first step in the automated ingestion of files containing tabular data. In this paper we present an algorithm that uses a logistic-regression classifier to evaluate whether a particular choice of delimiters is correct. The delimiter choice that is given the highest score by the classifier is chosen as the one most likely to be correct. The algorithm...
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